A nonlinear analytical redundancy method for sensor fault diagnosis in an automotive application

Abstract

Sensors have been widely implemented in vehicle systems for control, driving, and vehicle condition monitoring purposes. In a typical automotive vehicle, there are 60-100 sensors on board and is projected to reach 200 sensors per car. Those sensors provide rich information to ensure safe vehicle operation. However, like any dynamic systems, sensors are vulnerable to degrade or fail over time, which leads to the need of real-time sensor fault diagnosis. Analytical redundancy has been the key model-based approach for sensor fault diagnosis. However, existing analytical redundancy approaches are limited to linear systems, or some special cases of nonlinear systems. In this paper, the analytical redundancy approach is extended to nonlinear systems in general to ensure the accuracy of sensor measurements. Parity relations based on nonlinear observation matrix are formulated to characterize system dynamics and sensor measurements. Robust optimization is designed to identify the coefficient of parity relations that can tolerate certain level of measurement noise and model uncertainties. At last, sensor fault diagnosis in an air intake system is employed to demonstrate the effectiveness of the proposed method.